Deepchecks Framework for Evaluating RAG Systems

A novel framework named Deepchecks has been launched to assess Retrieval-Augmented Generation (RAG) systems, which integrate large language models with external knowledge retrieval. This framework tackles the difficulties in evaluating RAG applications stemming from the unpredictable nature of generated outputs and the intricate relationship between the retrieval and generation elements. Deepchecks utilizes a comprehensive strategy, including root cause analysis and monitoring in production, to ensure that it meets specific application requirements. Its goal is to establish a solid basis for evaluating reliability, relevance, and user satisfaction in RAG systems across various fields, including healthcare, finance, and customer service. The paper outlining Deepchecks has been submitted to arXiv and is now accessible for review.